Identifying Representational Biases in Datasets Using PCA: A Max-Disparity Partition Framework
Principal Component Analysis (PCA) minimises aggregate reconstruction error, which can inadvertently represent majority subgroups with substantially higher fidelity than minority subgroups. Fairness-aware extensions of PCA correct this disparity but require group labels as input. We address the logically prior question: given only a data matrix, which binary partition of the data suffers the greatest representational disparity under a shared PCA projection? We formalise this as the max-disparity partition problem and propose a greedy local-search algorithm, grounded in the Fiduccia-Mattheyses bipartitioning framework, that discovers the disparity-maximising partition without any predefine...
| Authors | Arjun KM, Shashi Jain |
|---|---|
| Primary category | stat.ML |
| Published | 21 September 2026 |
| Last updated at source | 21 September 2026 |
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